Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction

Abstract

MotivationComputational methods for compound-protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound-protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound-protein pairs with affinity and contact labels further limits the accuracy and the generalizability of CPAC models. ResultsTo overcome the aforementioned challenges of structure naivety and labelled-data scarcity, we introduce cross-modality and self-supervised learning, respectively, for structure-aware and task-relevant protein embedding. Specifically, protein data are available in both modalities of 1D amino-acid sequences and predicted 2D contact maps, that are separately embedded with recurrent and graph neural networks, respectively, as well as jointly embedded with two cross-modality schemes. Furthermore, both protein modalities are pretrained under various self-supervised learning strategies, by leveraging massive amount of unlabelled protein data. Our results indicate that individual protein modalities differ in their strengths of predicting affinities or contacts. Proper cross-modality protein embedding combined with self-supervised learning improves model generalizability when predicting both affinities and contacts for unseen proteins. AvailabilityData and source codes are available at https://github.com/Shen-Lab/CPAC. Contactyshen@tamu.edu Supplementary informationSupplementary data are included.